Abstract: Abstract：[Objective] In order to improve the accuracy and efficiency of ship trajectory anomaly detection, and solve the problems of traditional anomaly detection methods such as limited feature characterization ability, insufficient compensation accuracy, gradient disappearance and overfitting, an unsupervised ship trajectory anomaly detection method based on Transformer_LSTM codec model is proposed. [Method] Based on the encoder decoder architecture, the Transformer_LSTM module replaces the traditional neural network to achieve track feature extraction and track reconstruction. By embedding Transformer into the recursive mechanism of LSTM, combined with the cyclic unit and attention mechanism, self-attention and cross-attention can be used to calculate the state vector of cyclic unit and effectively construct the long sequence model. By minimizing the difference between the reconstructed output and the original input, the model learns the characteristics and motion mode of the general trajectory, and the trajectory whose reconstruction error is greater than the abnormal threshold is judged as the abnormal trajectory. [Results] The AIS data collected in January 2021 were adopted. The results showed that the accuracy rate, accuracy rate and recall rate of the model were significantly improved compared with those of LOF, DBSCAN, VAE, LSTM, etc. The F1 score was improved by 8.11% compared with that of VAE_LSTM model. [Conclusion] The anomaly detection performance of this method is significantly superior to the traditional algorithm in various indexes, and the model can be effectively and reliably applied to the trajectory anomaly detection of ships at sea.